






"""simple docstring"""
import inspect
import os
import unittest

import torch

import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
    execute_subprocess_async,
    require_cpu,
    require_huggingface_suite,
    require_multi_gpu,
    require_single_gpu,
)
from accelerate.utils import patch_environment







@require_huggingface_suite
class      UpperCamelCase_			(unittest.TestCase	):





	def 	_SCREAMING_SNAKE_CASE ( self   :					int		)		->     int:
				UpperCAmelCase_	:					List[Any]     	=   inspect.getfile(accelerate.test_utils		)
				UpperCAmelCase_	:					int     	=   os.path.sep.join(
				    mod_file.split(os.path.sep		)[:-1] + ["scripts", "external_deps", "test_metrics.py"]		)

				from accelerate.test_utils.scripts.external_deps import test_metrics  # noqa: F401

				UpperCAmelCase_	:					List[Any]     	=   test_metrics





	@require_cpu
	def 	_SCREAMING_SNAKE_CASE ( self   :					Union[str, Any]		)		->     Union[str, Any]:
				debug_launcher(self.test_metrics.main      ,      num_processes=1		)





	@require_cpu
	def 	_SCREAMING_SNAKE_CASE ( self   :					Any		)		->     str:
				debug_launcher(self.test_metrics.main		)





	@require_single_gpu
	def 	_SCREAMING_SNAKE_CASE ( self   :					Union[str, Any]		)		->     List[Any]:
				self.test_metrics.main()





	@require_multi_gpu
	def 	_SCREAMING_SNAKE_CASE ( self   :					List[Any]		)		->     Optional[int]:
				print(f"""Found {torch.cuda.device_count()} devices."""		)
				UpperCAmelCase_	:					List[str]     	=   ["torchrun", f"""--nproc_per_node={torch.cuda.device_count()}""", self.test_file_path]
				with patch_environment(omp_num_threads=1		):
							execute_subprocess_async(lowerCAmelCase_      ,      env=os.environ.copy()		)




"""simple docstring"""



from typing import Union

import fire
import torch
from tqdm import tqdm



def 	_lowerCamelCase(	a   ,    a = "cpu"   ,    a = None				):
       __a       =    torch.load(a   ,    map_location=a				)
       for k, v in tqdm(state_dict.items()				):
              if not isinstance(a   ,    torch.Tensor				):
                     raise TypeError("FP16 conversion only works on paths that are saved state dicts, like pytorch_model.bin"				)
              __a       =    v.half()
       if save_path is None:  # overwrite src_path
              __a       =    src_path
       torch.save(a   ,    a				)


if __name__ == "__main__":
   fire.Fire(convert)
